Autonomic management of autonomic systems

ABSTRACT

A method for the autonomic management of autonomic systems can include monitoring a managed system and recommending a course of action to be performed in the managed system. It can be determined whether the recommended course of action has been performed by an administrator. Responsive to the determination, it further can be determined whether an outcome from the course of action comports with a predicted outcome. If so, a point count can be changed responsive to a further determination that the outcome from the course of action comports with the predicted outcome. Conversely, the point count can be oppositely changed responsive to a further determination that the outcome from the course of action does not comports with the predicted outcome. In any case, when the point count crosses a threshold value, the management of the managed system can be transitioned to an adaptive component.

BACKGROUND OF THE INVENTION

1. Statement of the Technical Field

The present invention relates to the field of systems administration andmore particularly to the administration of an autonomic system.

2. Description of the Related Art

In the framed manifesto Paul Horn, Autonomic Computing: IBM'sPerspective on the State of Information Technology, (IBM Corporation2001) (hereinafter, the “Manifesto”), Paul Horn, Senior Vice Presidentof IBM Research, observed, “It's not about keeping pace with Moore'sLaw, but rather dealing with the consequences of its decades-longreign.” Given this observation, Horn suggested a computing parallel tothe autonomic nervous system of the biological sciences. Namely, whereasthe autonomic nervous system of a human being monitors, regulates,repairs and responds to changing conditions without any conscious efforton the part of the human being, in an autonomic computing system, thesystem must self-regulate, self-repair and respond to changingconditions, without requiring any conscious effort on the part of thecomputing system operator.

Thus, while the autonomic nervous system can relieve the human beingfrom the burden of coping with complexity, so too can an autonomiccomputing system. Rather, the computing system itself can bear theresponsibility of coping with its own complexity. The crux of theManifesto relates to eight principal characteristics of an autonomiccomputing system:

-   I. The system must “know itself” and include those system components    which also possess a system identify.-   II. The system must be able to configure and reconfigure itself    under varying and unpredictable conditions.-   III. The system must never settle for the status quo and the system    must always look for ways to optimize its workings.-   IV. The system must be self-healing and capable of recovering from    routine and extraordinary events that might cause some of its parts    to malfunction.-   V. The system must be an expert in self-protection.-   VI. The system must know its environment and the context surrounding    its activity, and act accordingly.-   VII. The system must adhere to open standards.-   VIII. The system must anticipate the optimized resources needed    while keeping its complexity hidden from the user.

Importantly, in accordance with the eight tenants of autonomiccomputing, several single system and peer-to-peer systems have beenproposed in which self-configuration, management and healing haveprovided a foundation for autonomic operation. Self-managing systemswhich comport with the principles of autonomic computing reduce the costof owning and operating computing systems. Yet, implementing a purelyautonomic system has proven revolutionary. Rather, as best expressed inthe IBM Corporation white paper, Autonomic Computing Concepts (IBMCorporation 2001) (hereinafter, the “IBM White Paper”), “Deliveringsystem wide autonomic environments is an evolutionary process enabled bytechnology, but it is ultimately implemented by each enterprise throughthe adoption of these technologies and supporting processes.”

In the IBM White Paper, five levels have been logically identified forthe path to autonomic computing. These five levels range from the mostbasic, manual process to the most purely autonomic. In furtherillustration, FIG. 1 is a block illustration of the five levels of thepath to autonomic-computing. The Basic Level 110 represents a startingpoint of information technology environments. Each infrastructureelement can be managed independently by an: administrator who canestablish, configure, monitor and ultimately replace the element. At theManaged Level 120, systems management technologies can be used tocollect information from disparate systems onto fewer consoles, reducingthe time consumed for the administrator to collect and synthesizeinformation as the environment becomes more complex.

Notably, the Predictive Level 130 incorporates new technologies toprovide a correlation among several infrastructure elements. Theseinfrastructure elements can begin to recognize patterns, predict theoptimal configuration of the system, and provide advice as to the natureof the course of action which the administrator ought to take. Bycomparison, at the Adaptive Level 140 the system itself canautomatically perform appropriate actions responsive to the informationcollected by the system and the knowledge of the state of the system.Finally, at the Autonomic Level 150 the entire information technologyinfrastructure operation is governed by business policies andobjectives. Users interact with the autonomic technology only to monitorthe business processes, alter the objects, or both.

Between each of the levels 110, 120, 130, 140, 150 of computingmanagement, thresholds 105, 115, 125, 135 exist. The transition from theBasic Level 110 through to the Autonomic Level 150 necessarily crosseseach threshold 105, 115, 125, 135 as the management principles vary frommanual characteristics 170 through to autonomic characteristics 180.Yet, the mechanism for automatically transitioning from one level to thenext has not been defined. In fact, often the level corresponding to amanagement configuration often is fixed from the start and cannot bevaried without substantial human intervention and reconfiguration.Certainly, the determination of when to transition from the PredictiveLevel 130 to the Adaptive Level 140 has not been defined. Nevertheless,it will be apparent to the skilled artisan that the primary differencebetween the Predictive Level 130 and the Adaptive Level is one of trustin the system's ability to manage its responsible elements without humanintervention.

SUMMARY OF THE INVENTION

The present invention addresses the deficiencies of the art in respectto the autonomic management of a system and provides a novel andnon-obvious method, system and apparatus for the autonomic management ofautonomic systems. For instance, in a preferred aspect of the presentinvention, the system can include a manual management process and anautonomic management process. Each of the manual and autonomicmanagement processes can have a configuration for recommending coursesof action responsive to monitoring the operation of a managed system. Adata structure further can be coupled to the manual and autonomicmanagement processes and configured for storing a point count reflectinga level of trust of decision making by the manual and autonomicmanagement processes. Finally, a transition process can be coupled tothe data structure and programmed to empower a selected one of themanual and autonomic management process to manage the managed systembased upon the data structure containing a point count which exceeds athreshold value.

By comparison, a method for the autonomic management of autonomicsystems can include monitoring a managed system and recommending acourse of action to be performed in the managed system. It can bedetermined whether the recommended course of action has been performedby an administrator. Responsive to the determination, it further can bedetermined whether an outcome from the course of action comports with apredicted outcome. If so, a point count can be changed responsive to afurther determination that the outcome from the course of actioncomports with the predicted outcome. Conversely, the point count can beoppositely changed responsive to a further determination that theoutcome from the course of action does not comport with the predictedoutcome. In any case, when the point count crosses a threshold value,the management of the managed system can be transitioned to an adaptivecomponent. Similarly, when the point count re-crosses the thresholdvalue in a direction opposite a direction which gave rise to thetransitioning step, management of the managed system can be returned toa predictive component. Hysterisis can be applied to eliminate overlyfrequent transitions.

BRIEF DESCRIPTION OF THE DRAWINGS

There are shown in the drawings embodiments which are presentlypreferred, it being understood, however, that the invention is notlimited to the precise arrangements and instrumentalities shown,wherein:

FIG. 1 is a block illustration of five levels of autonomic computingdefined within the art;

FIG. 2 is a schematic illustration of a system which has been configuredfor autonomic transitioning from a predictive to an adaptive managementstate in accordance with the inventive arrangements;

FIG. 3A is a flowchart illustrating a process for managing a systemelement in the predictive state of FIG. 2; and,

FIG. 3B is a flow chart illustrating a process for managing a systemelement in the adaptive state of FIG. 2.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present invention is a system, method and apparatus forautonomically transitioning between a manual and an autonomic managementscheme in an autonomic system. The manual management scheme can entailthe management of the system by an administrator in consultation withthe recommendations of a predictive management process. The autonomicmanagement scheme, by comparison, can entail the management of thesystem by an adaptive management process configured to act upon thecourse of action predicted by the process itself. The transition frommanual to autonomic management can be effectuated upon building a levelof trust in the predictive capabilities of the predictive managementprocess. Conversely, the transition from autonomic to manual managementcan be effectuated upon building a level of distrust in the predictivecapabilities of the autonomic management process.

FIG. 2 is a schematic illustration of a system which has been configuredfor autonomic transitioning from a predictive to an adaptive managementstate in accordance with the inventive arrangements. The system caninclude a managed system 210 which can be managed by one or moreauthorized administrators 220. The operation of the managed system 210can be monitored by a predictive management process 250 and an adaptivemanagement process 230.

Based upon the monitoring of the managed system 210, the predictivemanagement process 250 and the adaptive management process 240 canrecommend suitable management courses of action. In the case of thepredictive management process 250, the recommendations 260 can beforward to the administrator 220 responsive to which the administrator220 optionally can heed the recommendation 260 and issue a correspondingaction 270 in the management of the managed system 210. In contrast, theadaptive management process 240 can utilize its own recommendations 280to automatically perform a corresponding action 290 in the management ofthe managed system 210.

Importantly, a state transition processor 230 can monitor theperformance of both the predictive management process 250 and theadaptive management process 240. Where a certain level of trust has beenbuilt in respect to the accurate recommendations of the predictivemanagement process 250, the state transition processor 230 canautomatically transition the management of the managed system 210 fromthe ultimate manual control of the administrator 220 to the autonomiccontrol Of the adaptive management process 240. Conversely, when acertain level of distrust has been built in respect to the inaccuraterecommendations of the adaptive management process 240, the statetransition processor 230 can automatically transition the management ofthe managed system 210 from the ultimate autonomic control of theadaptive management process 240 to the manual control of theadministrator 220.

FIG. 3A is a flow chart illustrating a process for managing a systemelement in the predictive state of FIG. 2. Beginning in block 305, themanaged system can be observed. Based upon the state and operation ofthe managed system, in block 310, a recommendation can be formulated andforwarded to an administrator. The administrator can follow therecommendation by manually performing an action which comports with therecommendation, or the administrator can ignore or otherwise discountthe recommendation by not manually performing an action which comportswith the recommendation. In either case, the actions of theadministrator can be monitored in block 315.

If in decision block 320, if it is determined that the administrator didnot follow the recommendation of the predictive management process, themethod can return to block 305. Otherwise, if it is determined that theadministrator has followed the recommendation of the predictivemanagement process, in decision block 325 it can be determined whetherthe recommended action or actions of the administrator had an effectconsummate with the prediction of the predictive management process. Ifso, an indicator reflecting a level of trust of decision making by thepredictive management process, for instance, a “point count”, can beincremented. More specifically, each time the administrator successfullyfollows the recommendation of the predictive management process, theinstance can be tracked so as to build a history of trustworthiness ofthe predictive management process In contrast, each time the result ofthe administrator's having followed the recommendation of the predictivemanagement process does not comport with the prediction of thepredictive management process, the instance can be tracked so as tolimit the history of trustworthiness. Optionally, points can also besubtracted when an administrator does not follow the recommendation.

In decision block 335, if the incremented point count exceeds apredetermined threshold, in block 340 the system can transition to anadaptive management process, the requisite level of trustworthiness ofthe predictive capabilities of the autonomic system having satisfied thereservations of the administrator. Otherwise, the method can returnthrough block 305. Notably, the threshold can be pre-established toindicate a requisite level of trustworthiness preferred by anadministrator. The requisite level can range from loose trustworthinessto perpetual distrust. In the case of perpetual distrust, a sufficientlyhigh threshold value can ensure that the adaptive management processnever assumes control of the management of the managed system. In eithercase, the threshold can be pre-established for each action, for eachclass of action, or for each type of managed system.

FIG. 3B is a flow chart illustrating a process for managing a systemelement in the adaptive management process of FIG. 2. The autonomicstate of the adaptive management process can be reached through arequisite number of successful predictions of behavior in the managedsystem by the predictive state. Once in the autonomic state, in block350 the managed system once again can be observed. Responsive to eventsoccurring within and without the managed system, in block 355 theadaptive management process can set forth a recommended course ofaction. Subsequently, in block 360 the adaptive management process canperform the recommended course of action.

In decision block 365, it can be determined whether the actions of theadaptive management, process had an effect consummate with theprediction of the adaptive management process. If so, in block 370 thepoint count can be incremented further enhancing the trusting sentimentof the administrator. Subsequently, the process can return to block 350.Otherwise, when the result of the action undertaken by the adaptivemanagement process fails to comport with the prediction of the adaptivemanagement process, in block 375 the point count can be decrementedthereby indicating a lower level of trustworthiness arising from themistaken prediction of the adaptive management process.

In decision block 380, if the decremented point count falls below thepredetermined threshold, in block 385 the system can transition back tothe predictive management process, the requisite level oftrustworthiness of the predictive capabilities of the autonomic systemhaving not satisfied the requirements of the administrator. In thiscase, the administrator can be notified of the impending transition.Otherwise, the method can return through block 350. Importantly, as ahysterisis condition can arise from point values proximate to thethreshold, a smoothing function can be applied to point values proximateto the threshold. As an example, a trend which exceeds or falls belowthe threshold can be required before undertaking a transition.Additionally, certain actions can be forbidden to give rise to atransition, and other actions can be enumerated and excluded from theautonomic management, always remaining under explicit administratorcontrol.

The present invention can be realized in hardware, software, or acombination of hardware and software. An implementation of the methodand system of the present invention can be realized in a centralizedfashion in one computer system, or in a distributed fashion wheredifferent elements are spread across several interconnected computersystems. Any kind of computer system, or other apparatus adapted forcarrying out the methods described herein, is suited to perform thefunctions described herein.

A typical combination of hardware and software could be a generalpurpose computer system with a computer program that, when being loadedand executed, controls the computer system such that it carries out themethods described herein. The present invention can also be embedded ina computer program product, which comprises all the features enablingthe implementation of the methods described herein, and which, whenloaded in a computer system is able to carry out these methods.

Computer program or application in the present context means anyexpression, in any language, code or notation, of a set of instructionsintended to cause a system having an information processing capabilityto perform a particular function either directly or after either or bothof the following a) conversion to another language, code or notation; b)reproduction in a different material form. Significantly, this inventioncan be embodied in other specific forms without departing from thespirit or essential attributes thereof, and accordingly, referenceshould be had to the following claims, rather than to the foregoingspecification, as indicating the scope of the invention.

1. A system for the autonomic management of autonomic systemscomprising: a manual management process and an autonomic managementprocess, each of said manual and autonomic management processes having aconfiguration for recommending courses of action responsive tomonitoring the operation of a managed system; a data structure coupledto said manual and autonomic management processes and configured forstoring an indicator reflecting a level of trust of decision making bysaid manual and autonomic management processes; and, a transitionprocess coupled to said data structure and programmed to empower aselected one of said manual and autonomic management process to managesaid managed system based upon said data structure containing anindicator which exceeds a threshold level, wherein the autonomicmanagement process comprises the steps of: monitoring a managed systemand recommending a course of action to be performed in said managedsystem; determining whether said recommended course of action has beenperformed by an administrator and responsive to said determinationfurther determining whether an outcome from said course of actioncomports with a predicted outcome; changing a point count responsive toa further determination that said outcome from said course of actioncomports with said predicted outcome and oppositely changing said pointcount responsive to a further determination that said outcome from saidcourse of action does not comport with said predicted outcome; and, whensaid point count crosses a threshold value transitioning management ofsaid managed system to an adaptive component. 2-13. (canceled)